Venue traffic flow management

ABSTRACT

A platform provides recommendations for points of interest in a venue to venue attendees. Different points of interest are recommended in different amounts in order to prevent congestion in the venue in the form of extremely long queues or extremely large crowds. To achieve this, the platform divides a large group of venue attendees into multiple sub-groups, with each sub-group being recommended a different point of interest, and the size of each sub-group based on a difference between an optimal queue or crowd size and an actual queue or crowd size of a queue or crowd associated with that point of interest.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation and claims the prioritybenefit of U.S. patent application Ser. No. 16/010,349 filed Jun. 15,2018, now U.S. Pat. No. 10,438,141, which is a continuation-in-part ofU.S. patent application Ser. No. 15/828,120 filed Nov. 30, 2017 andclaims the priority benefit of U.S. provisional application 62/428,303filed Nov. 30, 2016, the disclosures of which are hereby incorporated byreference.

BACKGROUND 1. Field

The present teachings are generally related to an experience developmentand management platform. More specifically, the present teachings relateto development, deployment and real time management of highlypersonalized experiences occurring at managed locations.

2. Description of the Related Art

Locations that host visitors provide a wide range of experiences. Thevenues often have special events such as entertainment performances,provide attractions, such as rides, and provide various goods andservices, including foods, beverages, souvenirs and other merchandise,and many others. Items available at any given point of interest within alocation often change throughout a day or season, and other changingfactors, such as waiting lines, can further impact a guest or customerexperience. In such a complex and changing environment, it is verydifficult to provide visitors with relevant information at all times asthe visitors move from point-to-point within the location managed by ahost. Also, as many venues are large and complex, challenges exist forpersonnel to keep track of what is happening throughout a location at agiven time and to help each visitor have a favorable experiencethroughout a visit, and challenges exist for visitors to communicateamong themselves or communicate with the personnel of a venue. Mostlocations that host these experiences provide static paper maps,brochures or signs that provide guests information about a location andencourage engagement in one or more activities at the location. Someprovide additional information via website or apps that contain generalinformation about the venue and some updated information, such as eventschedules.

However, the quantity of experiences and points of interest at venuescan be overwhelming for venue attendees. Information concerning theseexperiences and points of interest can be difficult or impossible forattendees to find, as not all of it is publically accessible, and anyinformation that is available might be difficult to parse in anymeaningful way. As such, attendees, especially in groups, often mustspend long periods of time deliberating and planning routes anditineraries that makes sense based on their locations, their likes,their dislikes, and so forth—and even so, they still might make poorchoices in light of missing inventory at restaurants, long queues atcertain points of interest, or other discouraging situations. As such, asystem is needed to intelligently make recommendations to users and togroups of users based on different types of information about a venueand the user(s).

SUMMARY

A method for itinerary personalization for a first venue attendee in apredetermined venue area is claimed. The method includes identifying anumber of venue attendees in the plurality of venue attendees. Themethod also includes retrieving locations of a plurality of points ofinterest located within the predetermined event venue area, wherein eachof the plurality of points of interest corresponds to a queue of aplurality of queues. The method also includes identifying a plurality ofqueue sizes by identifying a queue size of each queue of the pluralityof queues. The method also includes calculating an optimal queue size tobe an average queue size based on a sum of the plurality of queue sizesand the number of venue attendees in the plurality of venue attendees.The method also includes dividing the plurality of venue attendees intoa plurality of groups, wherein each group of the plurality of groupscorresponds to one of the plurality of points of interest and to thequeue corresponding to the point of interest, wherein a size of eachgroup is based on a difference between the optimal queue size and thequeue size of to the queue corresponding to the group. The method alsoincludes generating a plurality of recommendations, wherein eachrecommendation recommends one of the plurality of points of interests.The method also includes sending the plurality of recommendations to aplurality of attendee mobile devices corresponding to the plurality ofvenue attendees so that a different point of interest of the pluralityof points of interest is recommended to each group of the plurality ofgroups based on the queue corresponding to the group.

A system that generates a personalized itinerary for a venue attendee ina predetermined venue area is claimed. The system includes a memory thatstores instructions and locations of a plurality of points of interestlocated within the predetermined event venue area, wherein each of theplurality of points of interest corresponds to a queue of a plurality ofqueues. The system also includes a processor, wherein execution of theinstructions by the processor causes the processor to perform systemoperations. The system operations include identify a number of venueattendees in the plurality of venue attendees. The system operationsalso include identifying a plurality of queue sizes, wherein each queueof the plurality of queues corresponds to a queue size of the pluralityof queue sizes. The system operations also include calculating anoptimal queue size to be an average queue size based on a sum of theplurality of queue sizes and the number of venue attendees in theplurality of venue attendees. The system operations also includedividing the plurality of venue attendees into a plurality of groups,wherein each group of the plurality of groups corresponds to a queue ofthe plurality of queues and wherein a size of the group is based on adifference between the optimal queue size and the queue sizecorresponding to the queue. The system operations also includegenerating a plurality of recommendations, wherein each recommendationrecommends one of the plurality of points of interests. The system alsoincludes a communication transceiver that sends the plurality ofrecommendations to a plurality of attendee mobile devices correspondingto the plurality of venue attendees so that a different point ofinterest of the plurality of points of interest is recommended to eachgroup of the plurality of groups based on the queue corresponding to thegroup.

Another method for itinerary personalization for a plurality of venueattendees in a predetermined venue area is claimed. The method includesidentifying a number of venue attendees in the plurality of venueattendees. The method also includes retrieving locations of a pluralityof points of interest located within the predetermined event venue area.The method also includes identifying a plurality of crowd sizes, whereineach of a plurality of crowds located within the predetermined venuearea corresponds to one of the plurality of crowd sizes. The method alsoincludes calculating an optimal crowd size to be an average crowd sizebased on a sum of the plurality of crowd sizes and the number of venueattendees in the plurality of venue attendees. The method also includesdividing the plurality of venue attendees into a plurality of groups,wherein each group of the plurality of groups corresponds to a point ofinterest of the plurality of points of interest and wherein a size ofthe group is based on a crowd of the plurality of crowds appearing alongat least one path to from the group to the point of interest and whereina size of the group is based on a difference between the optimal crowdsize and the crowd size corresponding to the crowd. The method alsoincludes generating a plurality of recommendations, wherein eachrecommendation recommends one of the plurality of points of interests.The method also includes sending the plurality of recommendations to aplurality of attendee mobile devices corresponding to the plurality ofvenue attendees so that a different point of interest of the pluralityof points of interest is recommended to each group of the plurality ofgroups based on the point of interest corresponding to the group.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 illustrates a system architecture for personalizing journeys anditineraries.

FIG. 2 illustrates a technology stack for real-time management ofexperiences with respect to personalized itineraries.

FIG. 3 illustrates information flow for real-time management ofexperiences and personalized itineraries.

FIG. 4 illustrates a dynamic live venue map identifying a personalizeditinerary in a theme park venue.

FIG. 5 illustrates a recommended itinerary map interface for a singleuser.

FIG. 6 illustrates a recommended itinerary map interface for two users.

FIG. 7 illustrates delivery of itinerary personalization to users.

FIG. 8 is a block diagram of an exemplary computing device that may beused to implement the present systems.

FIG. 9A illustrates a dynamic venue map identifying a crowd and its sizealong with four points of interest and corresponding queue sizes at eachof the points of interest.

FIG. 9B illustrates the dynamic venue map of FIG. 9A identifying optimalqueue sizes following dispersion of the crowd to the different points ofinterest.

FIG. 9C illustrates the dynamic venue map of FIG. 9A identifying optimaldispersion of the crowd to the different points of interest.

FIG. 10A illustrates a dynamic venue map identifying a five crowds andtheir corresponding sizes along with three points of interest.

FIG. 10B illustrates the dynamic venue map of FIG. 10A identifyingoptimal crowd sizes of the four smaller crowds assuming that the largestcrowd disperses toward the different points of interest but gets stuckin crowds along the way.

FIG. 10C illustrates the dynamic venue map of FIG. 10A identifyingoptimal dispersion of the largest crowd to the different points ofinterest assuming that the smaller crowds will absorb anyone movingthrough them.

DETAILED DESCRIPTION

A platform is described herein with various methods, systems,components, processes, services and the like that facilitate the design,creation, delivery and management of experiences at locations and venuesthat are optionally managed by a host. The platform generates apersonalized itinerary and corresponding map for a user within a venue.The personalized itinerary includes at least one recommended point ofinterest, the recommendation generated based on a location of therecommended point of interest relative to a location of the user, andbased on a comparison between user profile information concerning theuser and point of interest information concerning the recommended pointof interest. The recommended point of interest may also be recommendedbased on estimated wait times, queue lengths, or other informationobtained by the platform's servers.

Locations, areas, or venues as described herein may includeentertainment venues, malls, stores, theme parks, campuses, cruiseships, schools, universities, arenas, public parks, resorts, airports,terminals, tourist attractions, monuments, stations, markets, districts(e.g., municipal districts), stadiums, predetermined geographical areas,cruise routes, travel routes, cities, counties, countries, continents,or a combination thereof. These and other locations are collectivelyreferred to throughout this disclosure interchangeably as “venues,”,“areas,” or “locations,” and reference to any of the foregoing should beunderstood to encompass one or more of these, except where contextindicates otherwise. Such locations, areas, or venues are hosted byparties such as commercial enterprises, non-profit entities, educationalentities, and federal, state and municipal governments, collectivelyreferred to herein as “hosts,” “managers,” or “owners.” Such locationsattract and host thousands of people (referred to herein as “visitors,”“guests,” and “customers”) and provide a wide range of experiences. Suchlocations, areas, or venues may have predetermined boundaries that maybe used as geofence boundaries that, when crossed by a location-trackingdevice whose location is determined via Global Navigation SatelliteSystem (GNSS) or proximity to short-range wireless beacon devices, maytrigger actions as described further herein.

The platform of the present disclosure enables the creation ofpersonalized, relevant experiences that are delivered to guests at oneor more points of interest within a location at the right time, takinginto account a wide range of dynamic factors. By delivering a series ofsuch experiences over the course of a visit, through a dynamic,personalized itinerary, a host can provide guests with an optimizedoverall experience while using the resources that are required toprovide such experiences more efficiently and more profitably.

The present disclosure further includes a wide range of systems,methods, components, processes, and the like that facilitate thedevelopment and operation of the platform. For example, the platform mayinclude methods and systems for developing and managing a user profileor identity, such as based on demographic factors, past history, anduser behavior, such as to enable provision of personalized experiences,recommendations, itineraries and communications. The platform mayinclude facilities for automating the creation, assembly, delivery, andmanagement of experiences, including facilities for connecting to andintegrating with relevant systems (such as inventory systems, ticketingand entitlement management systems, reservations systems, schedulingsystems, and many others), for extracting, transforming and loading datato and from such systems, and for using machine learning to automate thecompletion of various methods, such as generation of relevantrecommendations, customization of communications, optimization ofmonetization, optimization of experiences, optimization of itineraries,and others.

The platform may further include various methods for facilitatingcommunication among hosts, personnel and visitors. These methods includedetermining that a user has entered a managed location by a computingdevice and identifying user contacts within the venue. The methodsfurther include transmitting messages and other content to computingdevices associated with each user contact within the venue regarding theuser within the venue.

The present teachings further include methods for providing a dynamicmap that is configured for display on a computing device, includingproviding graphical images of a venue and of relevant points of interestwithin the venue, with various interface elements, such as icons, logos,directional indicators, and the like that facilitate understanding aboutthe venue. The map may provide a navigation interface, such as forrouting a user to one or more points of interest in a location, such asguiding a user through one or more steps of an itinerary. The map mayfurther include providing a visual update of the user on the map as thevisitor moves through the venue and providing a personalized message orother content to the visitor regarding the venue, such as based on userdata collected while the user is in the venue or other information aboutthe user.

The present teachings also include methods for determining wait timesand using wait time information as a factor for designing and deliveringexperiences. The methods may include receiving direct wait time data fora point of interest, receiving location map data, and receivingadditional data including at least one of network traffic measurementdata, entitlement redemption data, and show or event schedule data. Themethods may further include determining a wait time and reporting thewait time to a remote device.

The present teachings may further include methods for engaging with auser within a venue. The method may include setting a first rule for apromotion provided by an application executing on a server. The firstrule may indicate to which users within a venue the promotion will beavailable. The method may further include communicating the promotion toa plurality of user devices within the venue and that correspond to thefirst rule. The user devices may be associated with users within thevenue. The method may also include updating the promotion before thepromotion ends for at least one of the plurality of users.

The terms “a” or “an,” as used herein, are defined as one or more thanone. The term “another,” as used herein, is defined as at least a secondor more. The terms “including” and/or “having”, as used herein, aredefined as comprising (i.e., open transition).

The present teachings generally include design, creation, development,assembly, provisioning, delivery and management of one or morepersonalized, timely experiences at one or more points of interest inone or more managed or hosted locations. For convenience these and otherelements and capabilities of the platform are collectively referred toherein as “management” or “real time management” of experiences, exceptwhere context indicates otherwise. A managed location can include any ofa wide variety of locations or venues as disclosed throughout thisdisclosure or known by those of skill in the art, such as a venue,stadium, arena, public park, public space or district, concert hall,amusement park, theme park, water park, block party, house party, beergarden, mall, store, monument, tourist attraction, and many others.Managed locations can also include multiple premises that as a whole canconstitute managed locations, such as, without limitation, a series offranchised locations. Managed locations can also include mobileplatforms such as watercraft, cruise liners, trains, and aircraft. Itwill be appreciated in light of the disclosure that an entertainmentvenue, such as a theme park, is but one managed location in which thepresent teachings can be implemented. Managed locations can also includelocations (one or many) of an enterprise, brand or other entity wherephysical assets of the enterprise are located. Managed locations canalso include any venue, store location, mall, theme park, city park,village, campus, cruise ship dock, airport terminals, parkingstructures, and many others.

A platform provides recommendations for points of interest in a venue tovenue attendees. Different points of interest are recommended indifferent amounts in order to prevent congestion in the venue in theform of extremely long queues or extremely large crowds. To achievethis, the platform divides a large group of venue attendees intomultiple sub-groups, with each sub-group being recommended a differentpoint of interest, and the size of each sub-group based on a differencebetween an optimal queue or crowd size and an actual queue or crowd sizeof a queue or crowd associated with that point of interest.

FIG. 1 illustrates a system architecture for personalizing journeys anditineraries. Uses of the system 100 of FIG. 1 may include live, dynamicmapping that utilizes branding, including hyper-local marketing. Thesystem 100 of FIG. 1 includes an ecosystem of data sources 105 such asmobile devices and/or wearable devices 110, point-of-entry/-exit (POE)terminals 115, point-of-sale (POS) terminals 117, and databases 120.Communicatively coupled to data sources 105 are back-end applicationservers 125. In system 100, application servers 125 can ingest,normalize and process data collected from mobile devices 110 and variousPOS or POE terminals 115. Types of information 140 gathered from datasources 105 and processed by back-end application servers 125 aregenerally inclusive of identity information such as user profiles,customer relationship management (CRM) data, entitlements, demographics,reservation systems and social media sources like Pinterest™ andFacebook™ data. Information 140 gathered from data sources 105 andprocessed by back-end application servers 125 also includesproximity/location information gathered using GNSS receivers such asGlobal Positioning System (GPS) receivers of mobile/wearable devices 100and/or via proximity between mobile/wearable devices 100 and beaconsthat emit short-range wireless signals (e.g., Bluetooth®, Bluetooth® LE,iBeacon, NFC, RFID, WiFi, radio). Information 140 gathered from datasources 105 and processed by back-end application servers 125 alsoincludes time-related data, such as schedules, weather, and queuelength.

Mobile and wearable devices 110 can execute applications via processorsthat make use of sensors and receivers of the respective mobile andwearable devices 110 to generate customer engagement data and then sharethat customer engagement data as the information 140 to the applicationserver(s) 125. The customer engagement data/information 140 may include,for example, current and prior physical locale within a venue as well aswait times and travel times (e.g., how long was a customer at aparticular point in a venue and how long did it take the customer totravel to a further point in a venue), paths to certain point on themap, and other information. Mobile devices 110 are inclusive of wearabledevices. Wearable devices (or ‘wearables’) are any type of mobileelectronic device that can be worn on the body or attached to orembedded in clothes and accessories of an individual, such aswristwatches, wristbands, armbands, chest bands, ankle bands, glasses,head-worn devices, devices integrated into clothing (including shoes,pants, shirts, jackets, hats, and others), and others. Processors andsensors associated with a wearable can gather, process, display, andtransmit and receive information, including location information, motioninformation and physiological information, among many other types.

With continued reference to FIG. 1, the POS data may be gathered atpoint of entry (POE) 115, or point of sale (POS) terminals 117 that mayinteract with a mobile or wearable device 110 to track customer purchasehistory at a venue or preference for engagement at a particular localewithin the venue. POE terminals 115 may provide data related to venuetraffic flow, including entry and exit data that can be inclusive oftime and volume. POE terminals 115 may likewise interact with mobile andwearable devices 110. POE terminals 115 and POS terminals 117 alike mayinclude or be connected to beacon devices using short-range wirelesscommunication transceivers to communicate with the mobile and wearabledevices 110 and thereby determine a location of the mobile and wearabledevices 110 (or velocity, or heading, or other information 140concerning the mobile and wearable devices 110) relative to knownlocation of the beacon based on a known signal strength (and a knownsignal range at the known signal strength) of the beacon.

Historical data may also be accessed at databases 120 as a part of theapplication server 125 processing operation. The results of a processingor normalization operation may likewise be stored for later access anduse. Processing and normalization results may also be delivered tofront-end applications (and corresponding application servers) thatallow for the deployment of contextual experiences and provide a networkof services to remote devices as is further described herein.

The present system 100 may be used with and communicate with any numberof external front-end devices 135 by way of a communications network130, either directly through the communication network 130 or softwaredevelopment kit (SDK) instructions called by particular softwareapplications (e.g., white label apps) run at the app server(s) 125, atthe front-end devices 135, at the data sources 105, at a device alongthe way in the communication network 130, or some combination thereof.The communications network 130 may be a local, proprietary network(e.g., an intranet) and/or may be a part of a larger wide-area network.The communication network 130 may include a variety of connectedcomputing devices that provide one or more elements of a network-basedservice. The communications network 130 may include actual serverhardware or virtual hardware simulated by software running on one ormore actual machines thereby allowing for software controlled scaling ina cloud environment.

The communications network 130 may allow for communication between datasources 105 and front-end devices 135 via any number of variouscommunication paths or channels that collectively make up thecommunications network 130. Such paths and channels may operateutilizing any number of standards or protocols including TCP/IP, 802.11,Bluetooth, iBeacon, GSM, GPRS, 4G, and LTE. The communications network130 may be a local area network (LAN) that can be communicativelycoupled to a wide area network (WAN) such as the Internet operatingthrough one or more network service provider.

Information received and provided over a communications network 130 maycome from other information systems such as GPS, cellular serviceproviders, or third-party service providers such as social networks. Thesystem 100 can measure location and proximity using hardware on a userdevice (e.g., GPS) or collect the data from fixed hardware andinfrastructure such as Wi-Fi positioning systems and Radio Frequency ID(RFID) readers. An exemplary location and proximity implementation mayinclude a Bluetooth low-energy or iBeacon beacon with real timeproximity detection that can be correlated to latitude/longitudemeasurements for fixed beacon locations.

Additional use cases may include phone-based, GPS, real-time location(latitude/longitude) measurements, phone geo-fence-real timenotifications when a device is moving into or out of location regions,Wi-Fi positioning involving user location detection based on Wi-Fisignal strength (both active or passive), RFID/Near Field Communication(NFC), and cellular tower positioning involving wide range detection ofuser device location, which may occur at the metro-level.

Front-end devices 135 are inclusive of kiosks, mobile devices, wearabledevices, venue devices, captive portals, digital signs, and POS and POEdevices. It should be noted that each of these external devices may beused to gather information about one or more consumers at a particularlocation during a particular time. Thus, a device that is providinginformation to a customer on the front-end (i.e., a front-end device135) such as a mobile device executing an application or a speciallydesigned wearable can also function as a data source 105 as describedabove. In some cases, front-end devices 135 may include any one of thedata sources 105 providing the information 140 to the app server 125,such as one of the mobile/wearable devices 110, one of the points ofexit/entry 115, one of the points of sale 117, or one of the databases120.

The system 100 of FIG. 1 provides services for personalizing journeysand itineraries. For example, a dynamic map including markerscorresponding to various captured photos, recorded videos, transactionreceipts, messages, social media posts, and other events may begenerated at a mobile computing device 110, at one or more applicationserver(s) 125, at one or more front-end-devices 135, or some combinationthereof. Any of the devices illustrated in FIG. 1, including the mobilecomputing devices 110, application server(s) 125, and front-end devices135 may include at least one computing system 800, or may include atleast some of the components illustrated in FIG. 8.

FIG. 2 illustrates a technology stack for real-time management ofexperiences with respect to personalized itineraries. More specifically,FIG. 2 depicts a physical world content management system 200. Thephysical world content management system 200 can include a dynamic venuemap 202 and various elements of a venue systems infrastructure 204. Themanagement system 200 can also include a mobile device 220 and awearable device 222. The management system 200 can also manage variousitineraries 228, such as for users or groups of users. The managementsystem 200 can include an experience as a service (EaaS) platform 230with a wide range of capabilities, as well as facilities for variousinterfaces to the platform 230, such as interfaces for analytics 256 andfor other users of an enterprise, such as through a corporate orcommercial viewer 254. The management system 200 may also include anEaaS software development kit (SDK) 250 for designing, developing andassembling experiences. The management system 200 can further include alive experience development application 252 for developing experiences.

The EaaS platform 230 of FIG. 2 may be run via the app server(s) 125 ofFIG. 1. The mobile device 220 and wearable device 222 of FIG. 2 may actas the mobile/wearable device 110 of FIG. 1 and/or the front-end device135 of FIG. 1. The locations system infrastructure 204 of FIG. 2 mayinclude the element(s) of the data sources 105 and/or the app server 125of FIG. 1.

The dynamic venue map 202 can connect to or be integrated with the EaaSplatform 230, such that the experiences created, delivered and managedusing the platform can include elements presented on the venue map 202,such as to visitors and to staff of the host. The SDK 250 anddevelopment application 252 may also generate and tweak a venue map 202,such as to allow developers to consider and integrate points ofinterest, routes, inventory locations, service locations, and otherfactors on the map when designing and delivering experiences. The venuemap 202 may integrate any of the information 140 discussed with respectto FIG. 1 and may also connect to or integrate with the systemsinfrastructure 204 of a location, such as to exchange data with elementsof the systems infrastructure 204 that are relevant to experiences, suchas sales and inventory data contained in point of sale (POS)infrastructure 212, to exchange location data with location-specific ornavigation infrastructure elements 208, such as beacons 216 and accesspoints 218 (e.g., WiFi hotspots and/or cell towers), and to coordinatewith content on media and signage infrastructure 214. The venue map 220may also connect to mobile devices 220, wearable devices 222 and theitineraries 228, so that map information can be presented on a visitor'sdevices, with appropriate itinerary information, messages, and the like,optionally presented in context using the map 202 as a presentationlayer. The itineraries 228 may be presented as lists, as a set ofdirections from the user's location to each consecutive POI/experience,or as a map that points out the user's location relative to eachconsecutive POI/experience (as in FIG. 4, FIG. 5, FIG. 6). In manyaspects of the present teachings, the dynamic venue map 202 can connectto the EaaS platform 230 through the physical world content managementsystem 238 and through use of the EaaS SDK 250, which may includevarious elements for creating, assembling, delivering and managingexperiences.

In aspects of the present teachings, the location systems infrastructure204 can include a venue inventory management system 206 and an indoornavigation infrastructure 208. Moreover, the location systemsinfrastructure 204 can make use of general networking infrastructure210. The location systems infrastructure 204 can include a POSinfrastructure 212, a media/display/signage infrastructure 214, beacons216, and access points 218. The venue inventory management systems 206can connect to the dynamic venue map 202, the indoor navigationinfrastructure 208 and general networking infrastructure 210. Thegeneral networking infrastructure 210 can connect to a POSinfrastructure 212, the media/display/signage infrastructure 214, thebeacons 216, the access points 218, and the like.

The mobile device 220 can include a mobile beacon 224. The mobile device220 can also connect to a wearable device 222 and can access theitineraries 228, such as to present an itinerary of a user or group onthe device. The mobile device 220 can also connect to the EaaS Platform230, the dynamic venue map 202, the location systems infrastructure 204and the EaaS SDK 250 to provide and receive data necessary to manage anexperience, to receive and redeem entitlements, to communicate aboutexperiences, to receive recommendations, to receive itineraries, and thelike. In additional aspects of the present teachings, the mobile device220 or wearable device 222 can connect to the location systemsinfrastructure 204, such as using the indoor navigation infrastructure208 or using other location capabilities, such as global positioningsystem (GPS) or cellular triangulation, so that the host can maintainprecise understanding of the user's location at all times. In aspects,the mobile device 220 of wearable device 222 can connect to the locationsystems infrastructure 204 through the venue inventory managementsystems 206, such as where the mobile device 220 or wearable device 222is used to order or purchase items, either at a point of sale or pointof interest, or remotely.

In various embodiments, the wearable device 222 can include or comprisea wearable beacon 226, such as to provide location information about theposition of the wearer of the wearable device 222, such as by detectingproximity to one or more points of interest (such as by detecting orinteracting with wireless infrastructure capabilities of the venue thatare known to be located at the points of interest via NFC, Bluetooth™,Bluetooth™ Low Energy (BTLE), WiFi, iBeacon, or other wireless signals,or other location methods, such as cellular triangulation, GPS, deadreckoning, user reporting of location, etc.). The wearable device 222can connect to or interact with the mobile device 220 and one or moreitineraries 228 (such as to receive information about current andupcoming itinerary items, such as the nature of the items, locations,times and routing information within the venue to the next or subsequentitem, as well as to receive information about entitlements, such astickets, that may be used, via the wearable device 222, in connectionwith items on the itinerary). The EaaS platform 230 can include orconnect to a reporting and analytics facility 232, which may takeinformation from various components of the EaaS platform 230, or otherelements that interact with the EaaS platform 230 and allow thegeneration of reports and analytic results on the data, such asinformation about what experiences have been recommended, whatexperiences have been undertaken by users, what entitlements have beenredeemed, what goods or services have been purchased, what profits havebeen made, and the like, in each case optionally presented by timeperiod, by location, by visitor, by group, by demographic factor, and bymany other variables. The analytics facility 232 may thus allow users,such as marketing staff of a host, to analyze the impact of any of thefactors captured in the various data sets used by the EaaS platform 230that may contribute to creation, recommendation, assembly, delivery, andcompletion of experiences, as measured by contribution of the factorsany of a wide variety of measures of performance (e.g., number ofvisits, duration of visits, frequency of visit, visitor satisfactionratings, profit per visitor, profit per time period, gross sales, netsales, gross profits, net profits, amounts paid by sponsors, rates paidby sponsors for sponsorship, and many other). Analytics may includevisualizations, such as heat maps, as well as presentations of resultsas an overlay on the dynamic venue map, such as showing whichattractions or points of interest are most popular, most profitable, orthe like. The analytics facility 232 may include capabilities forundertaking a wide range of analytic techniques, including A/B testingtechniques, correlation analysis (including use of similarity matrices,such as for collaborative filtering, as well as various knownstatistical techniques), analysis based on distributions (e.g., normaldistributions), probabilistic analysis (e.g., random walk and similaralgorithms), and many others. Output and results from the analyticsfacility 232 may be used to optimize recommendations, to suggest newexperiences, to improve performance of staff, to improve selection ofinventory, to optimize patterns of traffic within a venue, to improveprofits and yield, and for many other purposes.

Recommendations of experiences or points of interest (POI) may be madebased on user's location, on estimated time for the user to travel tothe point of interest, on wait times at the point of interest, on queuelengths at the point of interest, on popularity of the experience/POI,on the user never having been to the experience/POI before, on the userhaving been to the experience/POI more than a predetermined number oftimes already (indicating that the user likes the experience/POI), onexpected weather at the point of interest, on the experience or point ofinterest being related to something that the user likes according toprofile information concerning the user, on the experience or point ofinterest being unrelated to something that the user dislikes accordingto profile information concerning the user, on the type ofexperience/POI, on an amount of time since the was last at anexperience/POI of the same type exceeding a predetermined period oftime, on inventory at the POI, or some combination thereof.Recommendations of experiences and related points of interest may bemade based on such information concerning more than one user as well, sothat when a family is traveling together, for example, the likes anddislikes of each member of the family (and other information asdiscussed above concerning each member of the family) can be taken intoaccount to find optimal recommendations of experiences and points ofinterest.

A user's experience can importantly include experience with a particularvenue, or a particular type of venue. For example, the user profile canaccumulate, and reflect, the user's experience with a theme park, withtheme parks of a particular type, with a cruise ship, with visits tolocations within venues or around the world, and the like. Among otherthings, the user experience can keep track of what a user has doneduring past visits, including capturing positive and negative ratings,so that positive past experiences can be added to an itinerary orrecommended at appropriate times during a subsequent visit, or so thatsimilar experiences can be recommended or added to an itinerary at a newvenue. The user experience can also capture information about a currentvisit, such as indicating that a user has already experienced a certainattraction, event, service, or the like, so that the EaaS platform 230can steer the user to additional experiences or re-direct the user tofavorite experiences at appropriate times.

The user profile can also account for user interests, includinginterests in particular types of dining, foods or beverages, interestsin entertainment options (such as preferences in music, dancing, magicshows, animal shows, and many others), interests in attractions (such asthrill rides, water rides, arts, fireworks, fountains, historicalinformation, and many others), interests in particular characters,people or topics, and many others. These can be used, for example, toidentify relevant attractions that are either directly responsive to theuser's interests or that have been given positive ratings by similarusers. Interests can also be inferred, such as by identifying interestsof other users who have similar characteristics.

The EaaS platform 230 may include an assembly layer 234, and anexperience generator 236. The assembly layer 234 may be used to assemblean experience, such as by assembling various components that comprisethe experience, such as content (such as messaging and communications,recommendations, and the like, including multimedia content, brandedcontent, logos, and the like that may present aspects of theexperience), entitlements (such as tickets, reservations, coupons,discounts, passes (including line skipping passes) and the like, as wellas bar codes, QR codes, or other information needed to redeem anentitlement or undertake an experience), information about goods andservices (such as packages of foods and beverages that can be ready forthe visitor upon arrival), itinerary information (such as indicatingtime and place for the experience, wait time information, and the like),directional or navigation information, pricing information, and thelike. The assembly layer 234 may include user interfaces for a humanuser to assemble an experience, such as by authoring messages, selectingelements of an experience (including by menus, by drag-and-dropinterfaces that allow the user to pull items from libraries or database,and the like), setting parameters for the experience (such as pricingand discounts, timing factors (such as how long a discount isavailable), and the like. The assembly layer 234 may also includesemi-automated, or entirely machine-based assembly of experiences. Forexample, a machine learning capability of the assembly layer may use atraining set of assembled experiences as a basis for assembling(optionally under human supervision or with human confirmation)additional experiences that are similar to the ones created in thetraining set. Over time, the machine-based experience assemblycapability may use feedback (such as based on metrics indicatingsatisfaction by visitors with assembled experiences or indicatingprofitability or per-visitor yield for experiences) to improve thecapacity of the assembly layer 234 to assembly highly effectiveexperiences. The assembly layer 234 may also embody rules, such from arules engine 245, such as to mandate certain aspects of assembly ofexperiences or to preclude certain aspects of the assembly; for example,a rule might indicate that “no experience for a minor should includealcoholic beverages” or “all experiences between noon and 2:00 p.m.should include food and beverage recommendations.” Thus, through acombination of human creation, machine-automation and application ofrules, the assembly layer 234 allows the assembly of an experience. Theexperience generator 236 may take the experience assembled by theassembly layer 234 and generate a data structure reflecting the actualexperience, such as generating a message, with appropriate codes forredemption, entitlements, and the like, for delivery such as to thevisitor's mobile device or wearable device, and/or for delivery to apoint of sale, such as for use by staff of the host.

The EaaS platform 230 can also include or connect to a physical worldcontent management system 238, which may be used to manage variouscontent that is used to design, create, assemble, deliver, and recommendexperiences, such as multimedia content from various content libraries(e.g., branded content about products and services, attractions and thelike, video content about experiences, map content, content about pointsof interest (including location data, opening hours, mapping ofinfrastructure elements, such as beacons and displays, etc.)), as wellas information about visitors (such as user profile information asdiscussed in more detail below), information about other factors thatcan impact an experience (such as weather information), informationabout a venue (such as about available infrastructure, inventory, andthe like), information about the host, information about parameters ofexperiences (such as pricing, discounts, inventory levels, wait times,restrictions, prohibitions, and the like), and many other types ofcontent. The physical world content management system 238 is describedin more detail elsewhere in this disclosure. The EaaS platform 230 mayalso include a recommendation engine 240 for recommending experiences orpoints of interest (POI) or aspects of experiences, either directly to avisitor or to personnel of a host, such as to assist in assemblingexperiences or to assist staff in guiding visitors to favorableexperiences. The recommendation engine 240 is also described in moredetail elsewhere in this disclosure. The EaaS platform 230 may alsocreate, manage, and consume information from various userprofiles/identities 242, each of which may contain various identity,demographic, psychographic, geographic, historical, transactional,relational, social, personality, or other information that may indicatea user's likely preferences, relationships (such as membership in afamily social group, business group, or other group), or the like. Inthe context herein, the term “user” may refer to a venue attendee or auser of a particular mobile/wearable device 110 and/or front-end device135. The EaaS platform 230 can further include, connect to, or integratewith a context engine 243, which may be used to determine the context ofa visitor at a given time and place, such as taking into account thevisitor's identity, the time of day, the season, the weather, thepresence or proximity of various physical world elements (such as pointsof interest, displays, and infrastructure elements), the presence orproximity of other individuals (such as members of a family or socialgroup, or the like), the visitor's history (such as recent activities ortransactions, or longer-term activities that may indicate an interest ina type of activity), a visitor's current state (such as a level ofenergy or fatigue such as indicated by past activity (such asparticipation in physical exertion during hot weather)) or by tracking(such as by a wearable activity monitor) or a current mood (such asindicated by user survey), or by an indicator (such as from aphysiological monitor or facial recognition facility), or the like, orany of a wide variety of other elements that reflect the state orcontext of the visitor. The context engine 243 may include automatedelements, such as a machine learning facility, for automaticallydetermining, or predicting, a user's context, which may optionally betrained via a human-generated training set and optimized based onfeedback, such as indicators of the actual context of a visitor orindicators of particular factors used to determine that context. Forexample, visitors might provide feedback about energy levels, mood, orinterest that may be used to refine machine-learning models that infersuch factors based on other factors, such as time of day, weather, theconsumption foods, and the like. Output from the context engine 243 maybe provided to other aspects of the EaaS platform 230, such as theassembly layer, the experience generator, the SDK, the developmentapplication, and the like, so that experiences can be created that areappropriate for the context of a particular visitor or group.

The EaaS platform 230 may also include a sales activation engine 244,which may be used to assist personnel of the host in activating sales ofgoods and services that may be offered as part of or in conjunction withan experience. The sales activation engine 244 may take data from, forexample, an inventory system, so that a user of the sales activationengine 244 may be aware of what goods and services are in stock and atwhat levels, which goods and services need to be promoted (to maintainappropriate inventory levels), which ones are most complementary to eachother and to particular experiences or aspects of experiences, whichones are most profitable, and the like. A dashboard or interface of thesales activation engine 244 may allow personnel to determine what itemsshould be promoted given the current context of a visitor, including theidentity information, location, time of day, and other information andmay suggest assembly of an overall experience that is most likely topromote sales (and a high yield or profit). For example, a person whohas just waited in a long line on a hot day to participate in a thrillride may be offered a discounted favorite cold beverage (such as basedon past beverage purposes) at nearby point of interest, packaged with arecommendation for an experience that takes place there in an airconditioned environment. Embodiments of the sales activation engine 244may include automation, including machine learning, to automate therecommendation or assembly of items for sale, such as by using atraining set that is created by human personnel and subsequentlyoptimized based on feedback metrics, such as metrics on actual sales ofgoods and services, metrics on profitability, and the like. Rules fromthe rules engine 245 may be used to govern the use of the salesactivation engine 244, such as rules prohibiting certain types of salesactivation (e.g., “do not run more than 5 sales promotions per visitorper day” or “promote item X to all visitors today”). Output from thesales activation engine 244 may include direct messages to visitorspromoting items, as well as content for the assembly layer 234, such ascontent indicating what goods or services should be packaged or promotedwith a recommended experience.

As noted, the EaaS platform 230 may make use of rules, includingbusiness rules, in various components and methods described throughoutthis disclosure. Rules, such as business rules, may be developed,maintained and distributed using the rules engine 245. Rules may be usedto govern human-performed activities, such as ones used in variousinterfaces and dashboards to create, recommend, assemble, deliver andmanage experiences, as well as to govern automated activities, such assimilar ones performed based on machine learning.

The EaaS platform 230 can also connect to a beacon system 246, which maybe associated with a location database 248, such that locations ofbeacons 246 at various points of interest in a location can be known tothe EaaS platform 230, such as for use by humans, by machine-basedautomation or combination, as a basis for knowing where points ofinterest are and where other items (including detected items, suchmobile devices, wearable devices, and the like) are relative to thepoints of interest. The location database 248 may include locations ofbeacons, points of interest, inventory items, infrastructure elements(including communications infrastructure, media infrastructure, displayinfrastructure, facilities and the like), and the like. The locationdatabase 248 may include source information for use by various layers ofthe dynamic venue map as described in detail elsewhere in thisdisclosure.

The EaaS platform 230 may also include the EaaS SDK 250, which maycomprise a set of software tools, components, modules, libraries, andthe like for using the various aspects of the EaaS platform 230, such asfor taking outputs from and providing inputs to the context engine, therules engine, the location database, the assembly layer, the experiencegenerator, the recommendation engine, and other elements. This mayinclude various interfaces and similar elements for connecting to theEaaS platform 230 and its components, including APIs, connectors,gateways, buses, bridges, message brokers, hubs and the like. The SDKmay be used to create, assemble, deliver, and manage experiences, aswell as to manage and use aspects of the EaaS platform 230.

The EaaS platform 230 can connect to and use the itineraries 228, suchas to provide experiences to be included in an itinerary and to takeitinerary information as an input, such as to determine a next messageto provide to a user. Itineraries 228 are described in more detailelsewhere in this disclosure.

The EaaS platform 230 may interact with a mobile device 220 of a visitoror personnel of a host, such as for delivering messages and othercontent, delivering entitlements, redeeming entitlements, tracking userlocation, delivering sponsored content, and the like.

The EaaS platform 230 may use and integrate the dynamic venue map 202,such as presenting the map on a user device to show the venue, to guidevisitors to experiences, to show content about points of interest, toshow offers or promotions, to show sponsored content, or the like. Thedynamic venue map 202 may also be used as an interface for experiencedevelopers, such as using the SDK or the development application, wherestaff of a host may see where a visitor is located, see nearby points ofinterest, see inventory levels and the like, to help a staff memberdetermine an experience to recommend or provide information to a visitorabout an available experience. The dynamic venue map 202 may also beused for analytics, such as showing reports on various metrics that areassociated with points of interest at a location such as numbers ofvisits, sales levels, yields per visitor, inventory levels,profitability, and many others.

The EaaS platform 230 can also connect to the location systemsinfrastructure 204, such as to obtain current location data, such as thenumber of visitors detected in proximity to points of interest, and thelike. An analytics API 256 of the EaaS platform 230 may provide accessto various data from any of the components of the platform 230 forpurposes of analytics, including the analytics facility describedelsewhere in this disclosure and third party analytics facilities thatmay use the API to obtain information from the EaaS platform 230. TheEaaS platform 230 may include a user interface, such as a corporateviewer user interface (UI) 254, which may comprise a user interface bywhich staff, such as executive staff of a host, may see data aboutvarious aspects of the EaaS platform 230, including metrics onexperiences, visitors, visits, yields, and the like.

In embodiments, the analytics API 256 can connect to the EaaS platform230. The analytics API 256 can also connect to the EaaS Platform 230through the reporting and analytics facility 232. The corporate viewerUI 254 can connect to the EaaS platform 230. In further aspects, thecorporate viewer UI 254 can connect to the EaaS Platform 230 and makeuse of the reporting and analytics module 232.

Recommendations can also include coupons and promotions, sponsoredcontent, and the like. Available experiences can be delivered byoptional push notifications or can be made available to be pulled, suchas by interactions with a dynamic venue map 202, by searching (such asusing a mobile device), or the like. Recommendations can be sent byserver 125 to a staff member or to the guest. Recommendations can beintegrated into a personalized itinerary before being sent by server 125to a staff member or to the guest, the personalized itinerary optionallyincluding other experiences or POIs besides the experiences or POIsrecommended by the EaaS platform 230. Recommendations may also be basedon similarity or dissimilarity to other experiences or POIs in thepersonalized itinerary, such as experiences or POIs that were personallyselected by the user to whom the itinerary is personalized.

FIG. 3 illustrates information flow for real-time management ofexperiences and personalized itineraries. The information flow 300 ofFIG. 3 more specifically shows connections (each optionallybidirectional) between different types and/or sources of informationthat ultimately are used to produce a personalized itinerary 302/304 fora user 320. In relation to FIG. 1, any of the sources of information 140discussed with respect to FIG. 3 may be among any of data source(s) 105identified in FIG. 1 and/or may be stored at the app server(s) 125itself.

The EaaS platform 130, in the context of FIG. 3, can create and managean itinerary 302/304 for a user. The itinerary 302 can be for a fulljourney for a user within a venue, such as from a point of origin (e.g.,at home), to one or more locations/areas/venues (such as to a series ofcities on a trip, a series of islands or ports on a cruise as in FIG. 5or FIG. 6, or the like) and points of interest or sub-venues along theway (such as theme parks, ships, aircraft, ports, stores, malls,restaurants, bars, and many others). For each venue or sub-venue, anin-venue itinerary 304 can be generated, which in examples can be ahighly personalized personal itinerary 304 that is generated based onthe user profile 330 that may identify likes and dislikes and locationhistories of a user 320, as well as based on other information, such asfrom various third party data sources 340, which can include informationabout entitlements (such as tickets the user can have already obtainedto particular attractions or events, reservations the user can havealready made, and the like), information about expressed preferences,interests, or plans, information about climate and weather, and manyother items. Third party data sources 340 may include social mediaprofiles corresponding to users/attendees 320. Third party data sources340 may be part of the personal user profile 330, though they areillustrated separately in FIG. 3.

In examples, a live experience development application 352 can be usedto generate an in-venue personal itinerary 304 for the part of theitinerary 302 that is associated with a visit to a particular venue. Inaddition to using the user profile information 330 (which, as noted inthis disclosure, can account for relationships and connections with agroup, such that an itinerary for a user can be associated with, andmanaged in connection with, a larger group itinerary) and otherinformation about the full journey itinerary 302, the in-venue personalitinerary 304 can be dynamically created and account for other factors,such as current wait times and crowds (including adjusting the itinerary304 for flow control 308, so that users within a venue are spreadsmoothly across attractions, while still satisfying personal needs andgoals).

The itinerary 304 can also be repeatedly updated based on availableassets 312 (such as what attractions are open and have reasonable waittimes in the proximity of the user at a given time), the user's currentstate (such as whether the user expresses hunger, thirst, fatigue, orthe like, or such factors are inferred based on history), informationabout the user's group (such as locations and itineraries of the group),recommendations for the user (such as described in connection with therecommendation engine, including appropriate recommendations for theimmediate time and location as well as recommendations for laterexperiences. The itineraries 302 and 304 can be delivered and updated bythe messaging engine 310, such as by text or voice messages to theuser's mobile phone or wearable device, as well as by presentation ofinformation on the live, branded, dynamic venue map. For example, anitinerary can be shown on a map with the current location and currentlyrecommended item highlighted (such as in a color, with flashing symbols,or the like), and one or more alternative future recommendations shown,with routing information (including alternative available routes anditineraries) for the user.

The itinerary can show optional items as well as mandatory items (suchas the designated meetup location for a group). The EaaS platform 130,such as using the live experience development application 352 orautomatically, such as by machine learning, can constantly update theitinerary, such as accounting for changes. Changes can include the userhaving more time, because an attraction took less time than expected, orvice versa, or the user deciding to take a different route or dosomething different than was previously anticipated in the itinerary306. Changes can include changes in the inventory of tickets, goods,services, or the like near the user's location or at other locationsthat are later in the itinerary. Changes can include changes in waittimes. Changes can include changes in the user's state, such as becomingtired, hungry, thirsty, or bored. Changes can include changes initineraries for the user's group, changes in locations for members ofthe group, or the like. In each case, updated information can be used tosuggest a new itinerary that accounts for the current state of the userand the venue.

In many aspects, the EaaS platform 130 can track completion ofexperiences for an itinerary, such as by receiving location signals(such as an indication that a user's mobile phone or wearable device hasentered the proximity of a beacon that is positioned at a point ofinterest), by receiving evidence of redemption of entitlements (such asredemption of tickets, including electronic tickets, or redemption ofcredits, such as stored on a mobile device or wearable), and receivingevidence of purchases or consumption (such as indicated by transactiondata or other information from points of purchase located within avenue). Completed experiences can be recorded on the itinerary,prompting directions (such as messages or elements on a map, such asrouting information) for the next itinerary item, allowing theprogression through a series of locations, points of interest and thelike on the itinerary. As noted, changes in the venue, in the user'sstate, and other factors can lead to changes in the itinerary, which canbe managed automatically in the platform, can be managed by the user(such as by setting or approving items on the user's mobile phone), andcan be managed for the user by the experience provider, such as usingthe live experience development application 352.

In an example, a father and son are visiting a theme park. The liveexperience development application 352 is used to generate a fulljourney personal itinerary 302 for the father to follow during thevisit. The full journey personal itinerary 302 includes recommendationsbased on the personal user profiles 330 of the father and of the son.

For example, the personal user profiles 330 of the father and of the sonindicate the father-son relationship. The personal user profile 330 ofthe son indicates the son prefers to ride roller coasters, ride Ferriswheels and eat pizza. The personal user profile 330 of the fatherindicates the father has no ride preferences and prefers healthy eatingoptions.

The full journey personal itinerary 302 is also developed using datafrom third party data sources 340. For example, the father would like tominimize the cost of the trip and asked a question on a social networkabout how to minimize costs during a trip to a theme park. This questionis used to signal to the live experience development application 352 toinclude special deals and offers in the full journey personal itinerary302. Such information may alternately be included in the father's userprofile.

When in a group, all of this information concerning both the father andthe son may be used to generate recommendations for experiences and/orpoints of interest (POIs). For example, the son's preference for pizzaand the father's preference for healthy eating options may result in arecommendation for a restaurant that provides both pizza and healthyeating options. The son's preference for roller coasters and ferriswheels combined with the father's preference for minimizing costs of thetrip may result in a recommendation for the lowest-cost roller coasteror ferris wheel rides. This may be combined with locations of the fatherand son so as to recommend POIs that are near both the father and theson or along a route that they are known to be using based on other POIs(such as self-selected POIs) in their group itinerary or separaterespective itineraries.

FIG. 4 illustrates a dynamic live venue map identifying a personalizeditinerary in a theme park venue. The dynamic live venue map 400 of FIG.4 may be one implementation of a dynamic venue map 202 as discussed withrespect to FIG. 2 and/or a personalized itinerary 302/304 as discussedwith respect to FIG. 3.

Available assets 312 in the context of FIG. 4 indicate there are tworoller coasters (roller coaster A 402 and roller coaster B 404), aFerris wheel 406, a healthy eating restaurant 408, a theme park entrance412, and a parking lot 410 available at the theme park. The user'slocation 450 is marked on the map, with a solid line indicating that theuser has traveled from the parking lot 410 to the theme park entrance412 and is now standing between the theme park entrance 412 and theFerris wheel 406. The Ferris wheel 406, the healthy eating restaurant408 (optionally if the user is hungry as marked using a dashed box), andthe Roller coaster A 402 are included on the full journey personalitinerary 302 of FIG. 4. Available inventory 314 indicates theingredients for salads at a healthy eating restaurant 408 are nearingthe end of their shelf life, and this inventory information can be acomponent in recommending or not recommending the healthy eatingrestaurant 408. The healthy eating restaurant 408 is also included onthe full journey personal itinerary 302 of FIG. 4 for at least thisreason.

Presentation layer development can include the use of a dynamic/livevenue map 400. The dynamic/live venue map 400 can include point ofinterest (POI) locations 404, 402, 406, 408, 410, 412, which can bedynamically updated based on changes occurring within a venue or managedlocation. In many aspects, wait time can be an example of point ofinterest data or metadata associated with a location or point on thedynamic map. By way of this example, the point of interest data or mappoint metadata for one or more destinations can be pulled and updatedamong the users. In these examples, the point of interest data or mappoint metadata can include menu data for the restaurant, wait times formany points of interest at a managed location, or the like.

The personalized itinerary map of FIG. 4 may include recommendations forand based on two users, to relate FIG. 4 back to the father/son examplediscussed with respect to FIG. 3. The full journey personal itinerary302 is sent prior to the visit via the messaging engine 310 to thevisitor device/wearable 110 of the father, which is acting as afront-end device 135 in the context of FIG. 1. The full journey personalitinerary 302 can stretch back before arrival at the venue (the themepark) to also include directions to the theme park from the father's andson's home, as well as parking information. Parking information isupdated based on the available inventory 314 of parking. This guides thefather and son to an available parking lot 410, minimizing time theyhave to spend searching for an available parking space.

The EaaS platform 130 detects when the father and son enter the themepark, by detecting that a beacon on the mobile phone of the father hasentered the boundaries of the theme park. An in-venue personal itinerary304 is sent to the mobile phone of the father when the EaaS platform 130detects the father and son have entered the theme park.

The in-venue personal itinerary 304 includes a dynamic/live venue map400. In this example, roller coaster A 402, roller coaster B 404, Ferriswheel 406 and healthy eating restaurant 408 are displayed as POIlocations on the dynamic/live venue map 400.

The in-venue personal itinerary 304 suggests that the father and sonride roller coaster A 402 first, since roller coaster A is located closeto the theme park entrance 412. Because it is before noon, the in-venuepersonal itinerary 304 suggests the father and son ride roller coaster B404 second, before having lunch.

After riding roller coaster A 402, the father indicates in his userstate (which may be part of the user profile information) that thefather and son are hungry. Alternately, the EaaS platform may deducethat the father and son are likely hungry based on a time since theywere last at a food establishment exceeding a predetermined amount oftime (e.g, 3 hours). Based on this indication, a healthy eatingrestaurant 408 located in close proximity to the exit of Ferris Wheel406 is displayed as a dynamic map location 408 on dynamic/live venue map400.

The healthy eating restaurant 408 also serves salads. Because theingredients used to make salads are near the end of their shelf life, abuy-one-get-one-free salad offer is generated. Healthy eating restaurant408 is then highlighted on the dynamic/live venue map 400, indicating adeal on salads is available. The father chooses the healthy eatingrestaurant 408 for him and his son to have lunch and orders salads forboth of them, redeeming the buy-one-get-one-free salad offer.

In one example, the father and son may have had plans to ride rollercoaster B 404 after lunch. The EaaS platform 130 used wait time data orqueue length data from information 140 (as discussed with respect toFIG. 1) to detect that wait time at the Roller Coaster A 402 was short(or zero), while wait time at roller coaster B 404 was significant(e.g., exceeding a predetermined duration of time), indicating a shorterline at the Roller Coaster A 402 than at the Roller Coaster B 404, asthe father and son were finishing lunch. As the father and son werefinishing lunch, the dynamic map location of the Roller Coaster A 402 onthe dynamic/live venue map 400 turned green, indicating there was noline at the Roller Coaster A 402. Since there was no line at the Ferriswheel 406, the father and son decided to ride the Roller Coaster A 402after lunch, rather than ride roller coaster B 404.

With reference to FIG. 2, the present teachings generally include realtime management of highly personalized experiences of customers, theirfamilies, traveling companions or the like. The real time management ofhighly personalized experiences at managed locations can be integratedinto many of the operations and offerings at the managed locations. Theoperations and offerings at the managed locations can include events andentertainment offerings, but also includes fare offered by the location,and transport to and from the managed locations. With regard totransport to and from the managed locations, the real time management ofhighly personalized experiences of customers can coordinate the managedlocation to guide patrons to proper surface parking, garages, parkingtrams, or other forms of transportation that can be used.

The real time management of highly personalized experiences of customerscan connect to the computing devices of the customers and their familymembers, as well as computing devices of the host and its personnel,including staff at the venue and personal located remote from the venue.The computing devices can include phones, tablets, watches, wearabledevices (including ones dedicated to use at the venue), augmentedreality or virtual reality glasses, as well as laptop and desktopcomputers, servers, and the like. In connecting to the computing devicesof customers, cellular network and cloud network facilities can beemployed to ensure interconnection. The real time management of highlypersonalized experiences of customers can also connect with sponsors(such as advertisers) so as to be able to provide sponsored content tothe customers at the managed locations. The sponsored content can berelated in real-time to what is occurring with the user and/or thefamily of the user, their itinerary, the time of day and many factorsrelating to placement of sponsored content.

FIG. 5 illustrates a recommended itinerary map interface for a singleuser. The recommended itinerary map interface 500 of FIG. 5 may be oneimplementation of a dynamic venue map 202 as discussed with respect toFIG. 2 and/or a personalized itinerary 302/304 as discussed with respectto FIG. 3. In particular, the recommended itinerary map interface 500 ofFIG. 5 maps a personalized itinerary including recommended POIs anduser-selected POIs for a user “Matt” within a map 590 of a venueselected to be Eastern North America.

The recommended itinerary map interface 500 of FIG. 5 identifies a pastPOI 505 as the port of New York, which the user “Matt” was atpreviously. A first recommended POI 510 is provided by the EaaS platform230 as “Cruiseline Burger Grill.” The recommended POI 510 is recommendedbased on its location being along an existing route (from the user'scurrent location 530 to selected POI 515) and based on similarity tosomething that the user “Matt” likes according to his user profile(“Ike's Sadwiches”). A second POI 515 along the personalized itineraryis user-selected—Flamenco Beach in Culebra. A third POI 520 along thepersonalized itinerary is recommended by the EaaS platform 230 as “MiamiSurf Lessons” based on its location being along an existing route (fromthe user-selected POI 515 back to the port of New York 505) and based onsimilarity to the user-selected POI 515 (Flamenco Beach in Culebra) inthat both POIs are related to surfing.

The resulting map interface 500 illustrates a journey line 580representing a path connecting the POIs 505, 510, 515, 520, and back to505, the journey line 580 also connecting the current location 530 ofthe user. In some cases, a path 580 may be somewhat pre-set, as in apre-set cruise, in which case recommended POIs may be recommendedbecause they are along that path 580 and one or more additional reasons,such as similarity to “likes” of the user or dissimilarity to “dislikes”of the user or any of the other reasons identified above.

FIG. 6 illustrates a recommended itinerary map interface for two users.The recommended itinerary map interface 600 of FIG. 6 may be oneimplementation of a dynamic venue map 202 as discussed with respect toFIG. 2 and/or a personalized itinerary 302/304 as discussed with respectto FIG. 3. In particular, the recommended itinerary map interface 600 ofFIG. 6 maps a personalized itinerary including recommended POIs anduser-selected POIs for two users “Matt” and “Sam” within a map 690 of avenue selected to be Eastern North America.

The recommended itinerary map interface 600 of FIG. 6 identifies a pastPOI 605 as the port of New York, which the users “Matt” and “Sam” wereboth at previously. A first recommended POI 610 is provided by the EaaSplatform 230 as “Cruiseline Burger Grill.” The recommended POI 610 isrecommended based on its location being along an existing route (fromthe current location of both users 630 to selected POI 615) and based ondissimilarity to something that both users (“Matt” and “Sam”) dislikeaccording to their respective user profiles (sushi). A second POI 615along the personalized itinerary is user-selected—Flamenco Beach inCulebra. A third POI 620 along the personalized itinerary is recommendedby the EaaS platform 230 as “Miami Surf Lessons” based on its locationbeing along an existing route (from the user-selected POI 615 back tothe port of New York 605) and based on similarity to something that bothusers (“Matt” and “Sam”) like according to their respective userprofiles (sports).

While the recommended itinerary map interfaces 400, 500, 600, and 1000of FIG. 4, FIG. 5, FIG. 6, and FIG. 10 respectively, are all maps, itshould be understood that a personalized itinerary can take the form ofa list of POIs, either in order of when they should be visited or in adifferent order.

The resulting map interface 600 illustrates a journey line 680representing a path connecting the POIs 605, 610, 615, 620, and back to605, the journey line 680 also connecting the current location 630 ofthe users. In some cases, a path 680 may be somewhat pre-set, as in apre-set cruise, in which case recommended POIs may be recommendedbecause they are along that path 680 and one or more additional reasons,such as similarity to “likes” of one or more of the users ordissimilarity to “dislikes” of one or more of the users or any of theother reasons identified above.

FIG. 7 illustrates delivery of itinerary personalization to users.

With reference to FIG. 7, the present teachings generally include realtime management of highly personalized experiences of customers, theirfamilies, traveling companions or the like. The real time management ofhighly personalized experiences at managed locations can be integratedinto many of the operations and offerings at the managed locations. Theoperations and offerings at the managed locations can include events andentertainment offerings, but also includes fare offered by the location,and transport to and from the managed locations. With regard totransport to and from the managed locations, the real time management ofhighly personalized experiences of customers can coordinate the managedlocation to guide patrons to proper surface parking, garages, parkingtrams, or other forms of transportation that can be used.

The real time management of highly personalized experiences of customerscan connect to the computing devices of the customers and their familymembers, as well as computing devices of the host and its personnel,including staff at the venue and personal located remote from the venue.The computing devices can include phones, tablets, watches, wearabledevices (including ones dedicated to use at the venue), augmentedreality or virtual reality glasses, as well as laptop and desktopcomputers, servers, and the like. In connecting to the computing devicesof customers, cellular network and cloud network facilities can beemployed to ensure interconnection. The real time management of highlypersonalized experiences of customers can also connect with sponsors(such as advertisers) so as to be able to provide sponsored content tothe customers at the managed locations. The sponsored content can berelated in real-time to what is occurring with the user and/or thefamily of the user, their itinerary, the time of day and many factorsrelating to placement of sponsored content.

In short, the personalized itineraries 302/304 or maps 202 discussedherein may include recommended POIs based on any of the informationdiscussed in FIG. 7, including for example information concerningtransportation method, availability of rides using differenttransportation methods, scheduled events, campus entertainment, food andvending services inventory, location, wait times, parking locations andavailability, open parking space information, coordination with venuetransport, proximity to event location, vehicle and/or user and/ormobile device location, local relevant traffic information, advertisingcontent directed to customer or activity or event, sponsored POIs.

FIG. 8 illustrates an exemplary computing system 800 that may be used toimplement some aspects of the subject technology. For example, any ofthe computing devices, computing systems, network devices, networksystems, servers, and/or arrangements of circuitry described herein mayinclude at least one computing system 800, or may include at least onecomponent of the computer system 800 identified in FIG. 8. The computingsystem 800 of FIG. 8 includes one or more processors 810 and memory 820.Each of the processor(s) 810 may refer to one or more processors,controllers, microcontrollers, central processing units (CPUs), graphicsprocessing units (GPUs), arithmetic logic units (ALUs), acceleratedprocessing units (APUs), digital signal processors (DSPs), applicationspecific integrated circuits (ASICs), field-programmable gate arrays(FPGAs), or combinations thereof. Each of the processor(s) 810 mayinclude one or more cores, either integrated onto a single chip orspread across multiple chips connected or coupled together. Memory 820stores, in part, instructions and data for execution by processor 810.Memory 820 can store the executable code when in operation. The system800 of FIG. 8 further includes a mass storage device 830, portablestorage medium drive(s) 840, output devices 850, user input devices 860,a graphics display 870, and peripheral devices 880.

The components shown in FIG. 8 are depicted as being connected via asingle bus 890. However, the components may be connected through one ormore data transport means. For example, processor unit 810 and memory820 may be connected via a local microprocessor bus, and the massstorage device 830, peripheral device(s) 880, portable storage device840, and display system 870 may be connected via one or moreinput/output (I/O) buses.

Mass storage device 830, which may be implemented with a magnetic diskdrive or an optical disk drive, is a non-volatile storage device forstoring data and instructions for use by processor unit 810. Massstorage device 830 can store the system software for implementing someaspects of the subject technology for purposes of loading that softwareinto memory 820.

Portable storage device 840 operates in conjunction with a portablenon-volatile storage medium, such as a floppy disk, compact disk orDigital video disc, to input and output data and code to and from thecomputer system 800 of FIG. 8. The system software for implementingaspects of the subject technology may be stored on such a portablemedium and input to the computer system 800 via the portable storagedevice 840.

The memory 820, mass storage device 830, or portable storage 840 may insome cases store sensitive information, such as transaction information,health information, or cryptographic keys, and may in some cases encryptor decrypt such information with the aid of the processor 810. Thememory 820, mass storage device 830, or portable storage 840 may in somecases store, at least in part, instructions, executable code, or otherdata for execution or processing by the processor 810.

Output devices 850 may include, for example, communication circuitry foroutputting data through wired or wireless means, display circuitry fordisplaying data via a display screen, audio circuitry for outputtingaudio via headphones or a speaker, printer circuitry for printing datavia a printer, or some combination thereof. The display screen may beany type of display discussed with respect to the display system 870.The printer may be inkjet, laserjet, thermal, or some combinationthereof. In some cases, the output device circuitry 850 may allow fortransmission of data over an audio jack/plug, a microphone jack/plug, auniversal serial bus (USB) port/plug, an Apple® Lightning® port/plug, anEthernet port/plug, a fiber optic port/plug, a proprietary wiredport/plug, a BLUETOOTH® wireless signal transfer, a BLUETOOTH® lowenergy (BLE) wireless signal transfer, a radio-frequency identification(RFID) wireless signal transfer, near-field communications (NFC)wireless signal transfer, 802.11 Wi-Fi wireless signal transfer,cellular data network wireless signal transfer, a radio wave signaltransfer, a microwave signal transfer, an infrared signal transfer, avisible light signal transfer, an ultraviolet signal transfer, awireless signal transfer along the electromagnetic spectrum, or somecombination thereof. Output devices 850 may include any ports, plugs,antennae, wired or wireless transmitters, wired or wirelesstransceivers, or any other components necessary for or usable toimplement the communication types listed above, such as cellularSubscriber Identity Module (SIM) cards.

Input devices 860 may include circuitry providing a portion of a userinterface. Input devices 860 may include an alpha-numeric keypad, suchas a keyboard, for inputting alpha-numeric and other information, or apointing device, such as a mouse, a trackball, stylus, or cursordirection keys. Input devices 860 may include touch-sensitive surfacesas well, either integrated with a display as in a touchscreen, orseparate from a display as in a trackpad. Touch-sensitive surfaces mayin some cases detect localized variable pressure or force detection. Insome cases, the input device circuitry may allow for receipt of dataover an audio jack, a microphone jack, a universal serial bus (USB)port/plug, an Apple® Lightning® port/plug, an Ethernet port/plug, afiber optic port/plug, a proprietary wired port/plug, a BLUETOOTH®wireless signal transfer, a BLUETOOTH® low energy (BLE) wireless signaltransfer, a radio-frequency identification (RFID) wireless signaltransfer, near-field communications (NFC) wireless signal transfer,802.11 Wi-Fi wireless signal transfer, cellular data network wirelesssignal transfer, a radio wave signal transfer, a microwave signaltransfer, an infrared signal transfer, a visible light signal transfer,an ultraviolet signal transfer, a wireless signal transfer along theelectromagnetic spectrum, or some combination thereof. Input devices 860may include any ports, plugs, antennae, wired or wireless receivers,wired or wireless transceivers, or any other components necessary for orusable to implement the communication types listed above, such ascellular SIM cards.

Display system 870 may include a liquid crystal display (LCD), a plasmadisplay, an organic light-emitting diode (OLED) display, an electronicink or “e-paper” display, a projector-based display, a holographicdisplay, or another suitable display device. Display system 870 receivestextual and graphical information, and processes the information foroutput to the display device. The display system 870 may includemultiple-touch touchscreen input capabilities, such as capacitive touchdetection, resistive touch detection, surface acoustic wave touchdetection, or infrared touch detection. Such touchscreen inputcapabilities may or may not allow for variable pressure or forcedetection.

Peripherals 880 may include any type of computer support device to addadditional functionality to the computer system. For example, peripheraldevice(s) 880 may include a modem, a router, an antenna, a printer, abar code scanner, a quick-response (“QR”) code scanner, a document/imagescanner, a visible light camera, a thermal/infrared camera, anultraviolet-sensitive camera, a night vision camera, a light sensor, abattery, a power source, or some combination thereof.

The components contained in the computer system 800 of FIG. 8 are thosetypically found in computer systems that may be suitable for use withsome aspects of the subject technology and are intended to represent abroad category of such computer components that are well known in theart. Thus, the computer system 800 of FIG. 8 can be a personal computer,a hand held computing device, a telephone (“smart” or otherwise), amobile computing device, a workstation, a server (on a server rack orotherwise), a minicomputer, a mainframe computer, a tablet computingdevice, a wearable device (such as a watch, a ring, a pair of glasses,or another type of jewelry/clothing/accessory), a video game console(portable or otherwise), an e-book reader, a media player device(portable or otherwise), a vehicle-based computer, some combinationthereof, or any other computing device. The computer system 800 may insome cases be a virtual computer system executed by another computersystem. The computer can also include different bus configurations,networked platforms, multi-processor platforms, etc. Various operatingsystems can be used including Unix, Linux, Windows, Macintosh OS, PalmOS, Android, iOS, and other suitable operating systems.

In some cases, the computer system 800 may be part of a multi-computersystem that uses multiple computer systems 800, each for one or morespecific tasks or purposes. For example, the multi-computer system mayinclude multiple computer systems 800 communicatively coupled togethervia at least one of a personal area network (PAN), a local area network(LAN), a wireless local area network (WLAN), a municipal area network(MAN), a wide area network (WAN), or some combination thereof. Themulti-computer system may further include multiple computer systems 800from different networks communicatively coupled together via theinternet (also known as a “distributed” system).

Some aspects of the subject technology may be implemented in anapplication that may be operable using a variety of devices.Non-transitory computer-readable storage media refer to any medium ormedia that participate in providing instructions to a central processingunit (CPU) for execution and that may be used in the memory 820, themass storage 830, the portable storage 840, or some combination thereof.Such media can take many forms, including, but not limited to,non-volatile and volatile media such as optical or magnetic disks anddynamic memory, respectively. Some forms of non-transitorycomputer-readable media include, for example, a floppy disk, a flexibledisk, a hard disk, magnetic tape, a magnetic strip/stripe, any othermagnetic storage medium, flash memory, memristor memory, any othersolid-state memory, a compact disc read only memory (CD-ROM) opticaldisc, a rewritable compact disc (CD) optical disc, digital video disk(DVD) optical disc, a blu-ray disc (BDD) optical disc, a holographicoptical disk, another optical medium, a secure digital (SD) card, amicro secure digital (microSD) card, a Memory Stick® card, a smartcardchip, a Europay®/Mastercard®/Visa® (EMV) chip, a subscriber identitymodule (SIM) card, a mini/micro/nano/pico SIM card, another integratedcircuit (IC) chip/card, random access memory (RAM), static RAM (SRAM),dynamic RAM (DRAM), read-only memory (ROM), programmable read-onlymemory (PROM), erasable programmable read-only memory (EPROM),electrically erasable programmable read-only memory (EEPROM), flashEPROM (FLASHEPROM), cache memory (L1/L2/L3/L4/L5/L8), resistiverandom-access memory (RRAM/ReRAM), phase change memory (PCM), spintransfer torque RAM (STT-RAM), another memory chip or cartridge, or acombination thereof.

Various forms of transmission media may be involved in carrying one ormore sequences of one or more instructions to a processor 810 forexecution. A bus 890 carries the data to system RAM or another memory820, from which a processor 810 retrieves and executes the instructions.The instructions received by system RAM or another memory 820 canoptionally be stored on a fixed disk (mass storage device 830/portablestorage 840) either before or after execution by processor 810. Variousforms of storage may likewise be implemented as well as the necessarynetwork interfaces and network topologies to implement the same.

FIG. 9A illustrates a dynamic venue map identifying a crowd and its sizealong with four points of interest and corresponding queue sizes at eachof the points of interest.

In particular, the dynamic venue map 900 of FIG. 9A illustrates a crowd950 of venue attendees, the crowd 950 having a size 955 of 300 people(venue attendees). The four points of interest illustrated in dynamicvenue map 900 include a first castle-themed point of interest 910 with acorresponding queue 915 having 200 people (venue attendees), a secondanimal-themed point of interest 920 with a corresponding queue 925having 100 people (venue attendees), a third racing-themed point ofinterest 930 with a corresponding queue 935 having 50 people (venueattendees), and a fourth space-themed point of interest 940 with acorresponding queue 945 having 400 people (venue attendees).

Based on the location of the crowd 950 in between these four points ofinterest 910, 920, 930, and 940, it is fair to assume that at least amajority of the crowd 950 will end up dispersing to one of the fourqueues 915, 925, 935, or 945 corresponding to the four points ofinterest 910, 920, 930, and 940. That is, some of the crowd 950 may headtoward the first point of interest 910 and corresponding queue 915 viapath 960, some of the crowd 950 may head toward the second point ofinterest 920 and corresponding queue 925 via path 965, some of the crowd950 may head toward the third point of interest 930 and correspondingqueue 935 via path 970, and some of the crowd 950 may head toward thefourth point of interest 940 and corresponding queue 945 via path 975.

Two main issues arise in terms of how the crowd is likely to dispersealong paths 960, 965, 970, and 975. The first is that certain points ofinterest appear to be more popular than others, as evidenced by thequeue 945 corresponding to the fourth point of interest 940 having aqueue size of 400 people, more than any other queue pictured in FIG. 9A.If a majority of the crowd 950 simply heads along path 975 to the fourthpoint of interest 940 and corresponding queue 945, this will lead to thequeue 945 becoming extremely long while the other queues remainrelatively short, which increases risk of issues at point of interest940 such as mechanical failures for theme park rides or lack ofinventory or seating at a restaurant, bar, or shop. Movement of massivecrowds can cause traffic buildups, bottlenecks, and other issuesthroughout the venue that make the entire venue less efficient and canlead to increased risk along the entire path 975 and its vicinity.

Instead, it would be preferable to direct the crowd 950 to disperse in away that spreads the members of the crowd 950 to different points ofinterest, especially those that have shorter lines. This preventstraffic issues throughout the venue and can prevent overuse issues (suchas mechanical failure of lack of inventory) at highly popular points ofinterest and can prevent underuse issues (such as mechanical failure oroverabundance of unsold inventory) at less popular points of interest.

FIG. 9B illustrates the dynamic venue map of FIG. 9A identifying optimalqueue sizes following dispersion of the crowd to the different points ofinterest.

The calculations 905A, 905B, and 905C shown in FIG. 9B identify one wayof calculating an optimal queue size for the queues once the crowd 950of FIG. 9A is dispersed into the queues of FIG. 9A (915, 925, 935,and/or 945) assuming the dispersal is instantaneous—that is, notcounting any changes in queue size anticipated in the time it takes forthe members of the crowd 950 to reach the various queues along paths960, 965, 970, and/or 975. These calculation(s) are generally performedby the experience as a service (EaaS) platform 230 of FIG. 2 and/or theapp server 125 of FIG. 1.

The first calculation 905A serves is the base equation into which actualnumerical values from FIG. 9A are plugged into. The first calculation905A identifies that the optimal queue size is equal to a ratio, whereinthe numerator of the ratio is a sum of the queue sizes at issue added tothe crowd size 955 (here, 300 people), and wherein the denominator ofthe ratio is the number of queues at issue.

The second calculation 905B plugs in the values for all four queues 915,925, 935, and 945 into the equation of calculation 905A. The resultingcalculation is ((300+200+100+50+400)/4)=262.5. Thus, the optimal queuesize determined via the second calculation 905B is 262.5 people.However, 262.5 is less than 400, which is the queue size of the fourthqueue 945. Since our assumption here is that dispersion is instantaneousand therefore the queue size of the queue 945 will not shrink to 262.5,we try this calculation again in calculation 905C, this time omittingthe fourth queue 945 from the calculation.

The third calculation 905C plugs in the values for queues 915, 925, and935 into the equation of calculation 905A, this time omitting the fourqueue from consideration both in terms of the queue size in thenumerator of the ratio and the number of queues in the denominator ofthe ratio. The resulting calculation is ((300+200+100+50)/3)=216⅔. Thus,the optimal queue size determined via the second calculation 905C is216⅔ people, which is higher than all of the queue sizes of the threequeues 915, 925, and 935 taken into consideration in the thirdcalculation 905C. Thus, 216⅔ is the optimal queue size that we will usehere.

The calculations 905A, 905B, and 905C each represent an average—a meanin particular—of multiple queue sizes once the size 955 of the crowd 950of FIG. 9A is added in to those queues. To be clear the size 955 of thecrowd 950 is added into the numerator once in total—it is not addedrepeatedly so as to be added once per queue. Incorporation of the size955 of the crowd 950 does not affect the denominator of thisaverage/mean ratio that gives us the optimal queue size.

To optimally disperse the crowd 950 of FIG. 9A toward the differentqueues of FIG. 9A, the EaaS platform 230 should direct the crowd 950 ofFIG. 9A in a way that each of the three queues 915, 925, and 935 has216⅔ people. This is then achieved, as illustrated in FIG. 9C anddiscussed further herein, by dividing the crowd 950 of FIG. 9A intodifferent groups, and recommending different points of interest to eachgroup, with each group's size determined based on a difference betweenthe optimal queue size and the current queue size of that queue. Becausequeue 945 is already at 400 people, which is greater than the optimalqueue size of 216⅔ people, zero members of the crowd 950 of FIG. 9Ashould be directed toward or recommended to go to queue 945.

Obviously, a queue can only have an integer number of people, so itmakes little sense for a queue to aim for or target an optimal queuesize of 216⅔ people. Because we have three queues, however, this issueis easily resolved—two queues should aim for a target queue size of 217people (216⅔ rounded up to the next integer greater than 216⅔) and onequeue should aim for a target queue size of 216 people (216⅔ roundeddown to the next integer less than 216⅔). Which queues get rounded downor up can be decided at random. Alternately, and as illustrated in FIGS.9B and 9C, the queues that are currently the shortest can be chosen tohave a target queue size that is rounded up (here, to 217) while thequeues that are currently the longest can be chosen to have a targetqueue size that is rounded down (here, to 216). This may help especiallyif the points of interest corresponding to those queues are known to beunpopular or widely disliked and it can be assumed that some venueattendees might ignore or disregard recommendations to go to thosepoints of interest. Alternately, queue(s) that are farthest from anyknown congestion, such as the huge 400-person queue 945, can be chosento aim for a higher target queue size.

Wider variations in target queue sizes can also be used. For example,while FIG. 9B and FIG. 9C illustrate a target queue size of 216 peoplefor queue 915 and target queue sizes of 217 people for queues 925 and935, the EaaS platform 230 and/or app server 125 could alternatelyprovide a target queue size of 216 for queues 925 and 935 because theyare close to already-large queue 945, and provide a target queue size of218 for queue 915 because it is far from already-large queue 945.

FIG. 9C illustrates the dynamic venue map of FIG. 9A identifying optimaldispersion of the crowd to the different points of interest.

In particular, FIG. 9C illustrates the division by the EaaS platform 230and/or app server 125 of the crowd 950 of FIG. 9A into four groups 980,985, 990, and 995. The members of each of the four groups receiverecommendations to go to different points of interest.

That is, the 16 members of group 980 are each recommended to go to thefirst point of interest 910 along path 960, which would bring the queuesize of the queue 915 from 200 people to the target queue size of 216people as illustrated in FIG. 9B. The 117 members of group 985 are eachrecommended to go to the second point of interest 920 along path 965,which would bring the queue size of the queue 925 from 100 people to thetarget queue size of 217 people as illustrated in FIG. 9B. The 167members of group 990 are each recommended to go to the third point ofinterest 930 along path 970, which would bring the queue size of thequeue 935 from 50 people to the target queue size of 217 people asillustrated in FIG. 9B. Group 995 includes zero members, and as such,nobody in the crowd 350 is recommended to go to the point of interest940 along path 975, which would keep the queue size of the queue 945 at400.

While the calculations herein do not factor in changes in queue sizeover time as the groups from the crowd move along the paths 960, 965,970, 975—they may be modified to do so based on estimated movement speedof the members of the crowd 950, based on known traffic or congestion(or lack thereof) at different points in the venue that might affect theestimated movement speed, and based on estimated or observed speed orwait time of each of the different queues. Estimated movement speed ofthe members of the crowd 950 can be calculated on an individual basisfor each member based on recent or historical movement speeds, and canbe calculated differently based on movement type, such as walking,running, skateboarding, biking, taking a bus, taking a train, taking anautomobile, taking a ski lift, taking a boat, or taking a plane. Forexample, if queue 945 is known to be moving much more quickly thanqueues 915, 925, 935—for example, because point of interest 940 is avendor handing out free items without any need to use a register orpoint of sale device—the group 995 might have a few members of the crowd350 because the size of the queue 945 can be estimated to very soon dropbelow the optimal queue size calculated in calculation 905B, 262.5people.

While the term “queue” is used in FIG. 9A-9C and herein, it should beunderstood that this may also refer to crowds, groups, clusters, or anyother grouping of venue attendees or other individuals. Furthermore, itshould be understood the “crowd 350” was illustrated as a crowd for easeof discussion and need not be a crowd at all—in fact, the members of the“crowd 350” as discussed herein may be dispersed all throughout thevenue area. The members of the “crowd 350” as discussed herein may be,for example, at least a subset of all venue attendees who are notcurrently at a point of interest or in a queue or crowd.

FIG. 10A illustrates a dynamic venue map identifying a five crowds andtheir corresponding sizes along with three points of interest.

Like the dynamic venue map 900 of FIG. 9A, the dynamic venue map 1000 ofFIG. 10A illustrates a first crowd 1005 with a size 1010 of 200 people(venue attendees). The dynamic venue map 1000 also includes three pointsof interest—a first ghost-themed point of interest 1015, a seconddragon-themed point of interest 1020, and a third casino-themed point ofinterest 1025. The dynamic venue map 1000 also includes four smallercrowds along different paths in the venue—a second crowd 1030 with asize of 10 people (venue attendees), a third crowd 1035 with a size of50 people (venue attendees), a fourth crowd 1040 with a size of 30people (venue attendees), and a fifth crowd 1045 with a size of 70people (venue attendees).

Queues are not illustrated in or discussed regarding FIGS. 10A-C as theyare illustrated in and discussed regarding FIGS. 9A-C. Instead, FIGS.10A-C focus on minimizing overpopulation of any of the four smallercrowds 1030, 1035, 1040, and 1045 once the first crowd 1005 dispersestoward the three points of interest 1015, 1020, and 1025.

The dynamic venue map 1000 of FIG. 10A also illustrates paths 1080,1085, 1090, and 1095 from a location associated with the first crowd1005 to the three points of interest 1015, 1020, and 1025. Specifically,path 1080 leads from the location associated with the first crowd 1005to the first point of interest 1015, and passes through the second crowd1030 along the way. Path 1085 leads from the location associated withthe first crowd 1005 to the third point of interest 1025, and passesthrough the fourth crowd 1040 along the way. Path 1090 leads from thelocation associated with the first crowd 1005 to the third point ofinterest 1025, and passes through the third crowd 1035 along the way.Path 1095 leads from the location associated with the first crowd 1005to the second point of interest 1020, and passes through the third crowd1035 along the way. None of the paths 1080/1085/1090/1095 pass throughthe fifth crowd 1045.

FIG. 10B illustrates the dynamic venue map of FIG. 10A identifyingoptimal crowd sizes of the four smaller crowds assuming that the largestcrowd disperses toward the different points of interest but gets stuckin crowds along the way.

The assumption for calculations 1050A, 1050B, and 1050C shown in FIG.10B is that members of the first crowd 1005 have a high likelihood ofbeing absorbed into any of the smaller crowds that they pass through. Tothis end, the calculations 1050A, 1050B, and 1050C shown in FIG. 10Bidentify one way of calculating an optimal crowds size for the smallercrowds assuming that the smaller crowds absorb all of the members of thecrowd 1050 of FIG. 10A. Like the calculations of FIG. 9B, thecalculations of FIG. 10B assume dispersal is instantaneous—that is, thecalculations of FIG. 10B do not take into account any changes in thesizes of the smaller crowds anticipated in the time it takes for themembers of the crowd 1005 to reach the various smaller crowds alongpaths 1080, 1085, 1090, and/or 1095. These calculation(s) are generallyperformed by the experience as a service (EaaS) platform 230 of FIG. 2and/or the app server 125 of FIG. 1.

The first calculation 1050A serves is the base equation into whichactual numerical values from FIG. 10A are plugged into. The firstcalculation 1050A identifies that the optimal crowds size is equal to aratio, wherein the numerator of the ratio is a sum of the sizes of thefour smaller crowds and the size 1010 of the first crowd 1005 (here, 200people), and wherein the denominator of the ratio is the number ofsmaller crowds (here, four).

The second calculation 1050B plugs in the values for the four smallercrowds 1030, 1035, 1040, and 1045 into the equation of calculation1050A. The resulting calculation is (200+10+50+30+70)/4=90. Thus, theoptimal crowd size determined via the second calculation 1050B is 90people. However, because the fifth crowd 1045 does not appear along anypath, it is unlikely to absorb members of the crowd 1005 along theirpaths to the points of interest. Therefore, in calculation 1050C, thefifth crowd 1045 is omitted.

The third calculation 1050B plugs in the values for the second crowd1030, the third crowd 1035, and the fourth crowd 1040, into the equationof calculation 1050A, but omits the fifth crowd 1045 as explained above.The resulting calculation is (200+10+50+30)/3=96⅔. Thus, the optimalcrowd size determined via the third calculation 1050C is 96⅔ people.

The calculations 1050A, 1050B, and 1050C each represent an average—amean in particular—of multiple sizes of the smaller crowds once thefirst crowd 1005 of FIG. 10A is added in to those smaller crowds.

FIG. 10C illustrates the dynamic venue map of FIG. 10A identifyingoptimal dispersion of the largest crowd to the different points ofinterest assuming that the smaller crowds will absorb anyone movingthrough them.

To optimally disperse the first crowd 1005 of FIG. 10A into the smallercrowds, the EaaS platform 230 should direct the first crowd 1005 in away that each of the second crowd 1030, the third crowd 1035, and thefourth crowd 1040 has the optimal crowd size of 96⅔ people. As discussedwith respect to the queues of FIG. 9C, the true target size for each ofthese smaller crowds may be rounded up to the nearest integer greaterthan the optimal crowd size, rounded down to the nearest integer lessthan the optimal crowd size, or otherwise adjusted based on estimatedrate of travel of each of the members of the first crowd 1005, estimatedrates of change in size of each of the smaller crowds, estimatedmovements of each of the smaller crowds, sizes of any queues at thepoints of interest, estimated rates of movement of any queues at thepoints of interest, estimated wait times of any queues at the points ofinterest, and so forth.

To accomplish this, the EaaS platform 230 divides the first crowd 1005into different groups 1055, 1070, and 1075, and recommending differentpoints of interest to each group, with each group's size determinedbased on a difference between the optimal crowd size and the currentcrowd size of crowds along paths to the point of interest.

First, the first crowd 1005 is actually divided into groups 1055, 1060,1065, and 1075, wherein each of these groups takes a different one ofthe paths 1080, 1085, 1090, and 1095, respectively. That is, group 1070takes path 1080, and therefore the size of group 1070 (87 people) is thedifference of the target crowd size of the second crowd 1030 (97 people)and the actual crowd size of the second crowd 1030 (19 people). Group1065 takes path 1085, and therefore the size of group 1065 (67 people)is the difference of the target crowd size of the fourth crowd 1040 (97people) and the actual crowd size of the fourth crowd 1040 (30 people).Group 1060 takes path 1090 and group 1055 takes path 1095. Both paths1090 and 1095 pass through the third crowd 1035, therefore the size ofgroups 1055 and 1060 (23 people) is one half to the difference of thetarget crowd size of the third crowd 1035 (96 people) and the actualcrowd size of the third crowd 1035 (50 people). Because paths 1085 and1090 both go to the third point of interest 1025, groups 1060 and 1065are then merged together to form overarching group 1075 having 90 people(the sum of the sizes of groups 1060 and 1065).

The EaaS platform 230 and/or app server 125 then automatically generatesand sends recommendations to each of the groups 1055, 1070, and 1075 togo to different points of interest. In particular group 1055 receives arecommendation to go to the first point of interest 1015. Group 1055receives a recommendation to go to the second point of interest 1020.Group 1075 receives a recommendation to go to the third point ofinterest 1025.

While queues are not illustrated in or discussed regarding FIGS. 10A-Cas they are illustrated in and discussed regarding FIGS. 9A-C, it shouldbe understood that the queue management of FIGS. 9A-C may be combinedwith the crowd management of FIGS. 10A-C.

While various flow diagrams provided and described above may show aparticular order of operations performed by some embodiments of thesubject technology, it should be understood that such order isexemplary. Alternative embodiments may perform the operations in adifferent order, combine certain operations, overlap certain operations,or some combination thereof.

The foregoing detailed description of the technology has been presentedfor purposes of illustration and description. It is not intended to beexhaustive or to limit the technology to the precise form disclosed.Many modifications and variations are possible in light of the aboveteaching. The described embodiments were chosen in order to best explainthe principles of the technology, its practical application, and toenable others skilled in the art to utilize the technology in variousembodiments and with various modifications as are suited to theparticular use contemplated. It is intended that the scope of thetechnology be defined by the claim.

While the foregoing written description enables one skilled in the artto make and use what is considered presently to be the best modethereof, those skilled in the art will appreciate in light of thedisclosure that the existence of variations, combinations, andequivalents of the specific aspects, embodiments, structures, modules,methods, and examples herein. The disclosure should therefore not belimited by the above described examples, but by all aspects of thepresent teachings within the scope and spirit of the disclosure.

What is claimed is:
 1. A method for itinerary personalization in apredetermined venue area, the method comprising: generating one or morerecommendations for a first point of interest located within thepredetermined venue area, the first point of interest associated with afirst level of congestion; generating one or more recommendations for asecond point of interest located within the predetermined venue area,the second point of interest associated with a second level ofcongestion that is greater than the first level of congestion; dividinga plurality of venue attendee devices within the predetermined venuearea into at least a first group of venue attendee devices and a secondgroup of venue attendee devices, wherein the first group of venueattendee devices includes at least one more venue attendee device thanthe second group of venue attendee devices does based on the secondlevel of congestion being greater than the first level of congestion;sending the one or more recommendations for the first point of interestto the first group of venue attendee devices; and sending the one ormore recommendations for the second point of interest to the secondgroup of venue attendee devices.
 2. The method of claim 1, wherein thefirst level of congestion corresponds to a length of a first queue atthe first point of interest, and wherein the second level of congestioncorresponds to a length of a second queue at the second point ofinterest.
 3. The method of claim 2, wherein a sum of the length of thefirst queue and a size of the first group of venue attendee devices isequal a sum of the length of the second queue and a size of the secondgroup of venue attendee devices.
 4. The method of claim 1, wherein thefirst level of congestion corresponds to congestion along a first pathassociated with the first point of interest, and wherein the secondlevel of congestion corresponds to congestion along a second pathassociated with the second point of interest.
 5. The method of claim 1,wherein the first level of congestion corresponds to a size of a firstcrowd at the first point of interest, and wherein the second level ofcongestion corresponds to a size of a second crowd at the second pointof interest.
 6. The method of claim 1, wherein the first level ofcongestion corresponds to a popularity of the first point of interest,and wherein the second level of congestion corresponds to a popularityof the second point of interest.
 7. The method of claim 1, wherein thefirst level of congestion corresponds to a risk of mechanical failure ofthe first point of interest, and wherein the second level of congestioncorresponds to a risk of mechanical failure of the second point ofinterest.
 8. The method of claim 1, further comprising: generating oneor more recommendations for a third point of interest located within thepredetermined venue area, wherein dividing the plurality of venueattendee devices into at least the first group of venue attendee devicesand the second group of venue attendee devices includes dividing theplurality of venue attendee devices into at least the first group ofvenue attendee devices and the second group of venue attendee devicesand a third group of venue attendee devices; and sending the one or morerecommendations for the third point of interest to the third group ofvenue attendee devices.
 9. The method of claim 8, wherein the thirdpoint of interest is associated with a third level of congestion, andwherein a number of venue attendee devices in the third group of venueattendee devices relative to a number of venue attendee devices in thefirst group of venue attendee devices and a number of venue attendeedevices in the second group of venue attendee devices is based on acomparison between the third level of congestion and the first level ofcongestion and the second level of congestion.
 10. The method of claim1, wherein dividing the plurality of venue attendee devices into the atleast the first group of venue attendee devices and the second group ofvenue attendee devices is based on respective locations of the pluralityof venue attendee devices, a location of the first point of interest,and a location of the second point of interest.
 11. The method of claim10, wherein the respective locations of a plurality of venue attendeedevices include respective locations of the first group of venueattendee devices and respective locations of the second group of venueattendee devices, wherein dividing the plurality of venue attendeedevices into at least the first group of venue attendee devices and thesecond group of venue attendee devices is based on the respectivelocations of the first group of venue attendee devices being closer tothe location of the first point of interest than the respectivelocations of the second group of venue attendee devices and on therespective locations of the second group of venue attendee devices beingcloser to the location of the second point of interest than therespective locations of the first group of venue attendee devices. 12.The method of claim 10, wherein the locations of the plurality of venueattendee devices include a location that is identified using one or moreGlobal Navigation Satellite System (GNSS) receivers of one of theplurality of venue attendee devices.
 13. The method of claim 10, whereinthe locations of the plurality of venue attendee devices include alocation that is identified based on one of the plurality of venueattendee devices being in communication range of a short-range wirelesssignal source having a known location.
 14. A system for itinerarypersonalization in a predetermined venue area, the system comprising: amemory that stores instructions; a processor, wherein execution of theinstructions by the processor causes the processor to: generate one ormore recommendations for a first point of interest located within thepredetermined venue area, the first point of interest associated with afirst level of congestion, generate one or more recommendations for asecond point of interest located within the predetermined venue area,the second point of interest associated with a second level ofcongestion that is greater than the first level of congestion, anddivide a plurality of venue attendee devices within the predeterminedvenue area into at least a first group of venue attendee devices and asecond group of venue attendee devices, wherein the first group of venueattendee devices includes at least one more venue attendee device thanthe second group of venue attendee devices does based on the secondlevel of congestion being greater than the first level of congestion;and a communication transceiver, wherein the communication transceiversends the one or more recommendations for the first point of interest tothe first group of venue attendee devices and sends the one or morerecommendations for the second point of interest to the second group ofvenue attendee devices.
 15. The system of claim 14, further comprising abeacon that emits a short-range wireless signal, wherein execution ofthe instructions by the processor causes the processor to identify oneor more locations of one or more of the plurality of venue attendeedevices based on the one or more of the plurality of venue attendeedevices being in range of the beacon, wherein dividing the plurality ofvenue attendee devices into at least the first group of venue attendeedevices and the second group of venue attendee devices is based on thelocations of one or more of the plurality of venue attendee devices, alocation of the first point of interest, and a location of the secondpoint of interest.
 16. The system of claim 14, wherein the first levelof congestion corresponds to a length of a first queue at the firstpoint of interest, and wherein the second level of congestioncorresponds to a length of a second queue at the second point ofinterest.
 17. A method for itinerary personalization, the methodcomprising: dividing a plurality of venue attendee devices within thepredetermined venue area into at least a first group of venue attendeedevices and a second group of venue attendee devices, wherein the firstgroup of venue attendee devices includes at least one more venueattendee device than the second group of venue attendee devices doesbased on a second level of congestion associated with a second point ofinterest being greater than a first level of congestion associated witha first point of interest; sending one or more recommendations for thefirst point of interest to the first group of venue attendee devices;and sending one or more recommendations for the second point of interestto the second group of venue attendee devices.
 18. The method of claim17, wherein the first level of congestion corresponds to a length of afirst queue at the first point of interest, and wherein the second levelof congestion corresponds to a length of a second queue at the secondpoint of interest.
 19. The method of claim 17, wherein the first levelof congestion corresponds to congestion along a first path associatedwith the first point of interest, and wherein the second level ofcongestion corresponds to congestion along a second path associated withthe second point of interest.
 20. The method of claim 17, whereindividing the plurality of venue attendee devices into at least the firstgroup of venue attendee devices and the second group of venue attendeedevices is based on locations of the plurality of venue attendeedevices, a location of the first point of interest, and a location ofthe second point of interest.